Disease detection in infected plant leaf by computational method

Abstract

Plant diseases can directly affect the production hampering the economy significantly. Thus, early and correct detection of the disease is always a priority for an agriculture-dependent state. Of the many modern techniques of early detection of plant diseases, image processing has become a potential tool through which not only the disease can be detected early and correctly, but also it can be quantified successfully. The detection of two most important diseases of rice i.e., brown spot and rice blast was done through efficient computational method using a low complexity radial basis function neural network (RBFNN) classifier. The performance was analyzed using quality measures viz. accuracy, precision and recall and found to be 95%, 97% and 95%, respectively.

Keywords:

• Plant disease
• image processing
• rice
• brown spot
• rice blast
• RBFNN

Acknowledgements

The authors are indebted to IRRI Rice Knowledge Bank by www.knowledgebank.irri.org) and Dr. Don Groth for the kind permissions to use their images Spindle shaped lesions-Rice Blast and Closeup view of Brown spot of Rice leaf, respectively, in the article. The authors also sincerely thank the management of Centurion University of Technology and Management, Odisha, India for the support extended during the research work.

Disclosure statement

No potential conflict of interest was reported by the authors.